Download Use of procalcitonin for the diagnosis of pneumonia in patients

European Journal of Heart Failure (2012) 14, 278–286
doi:10.1093/eurjhf/hfr177
Use of procalcitonin for the diagnosis of
pneumonia in patients presenting with a chief
complaint of dyspnoea: results from the BACH
(Biomarkers in Acute Heart Failure) trial
Alan Maisel 1,2*, Sean-Xavier Neath 2, Judd Landsberg 1,2, Christian Mueller 3,
Richard M. Nowak 4, W. Frank Peacock 5, Piotr Ponikowski 6, Martin Möckel 7,
Christopher Hogan 8, Alan H.B. Wu 9, Mark Richards 10, Paul Clopton 1,
Gerasimos S. Filippatos 11, Salvatore Di Somma 12, Inder Anand 13, Leong L. Ng 14,
Lori B. Daniels 9, Robert H. Christenson 15, Mihael Potocki 3, James McCord 4,
Garret Terracciano 16, Oliver Hartmann 17, Andreas Bergmann 18,
Nils G. Morgenthaler 7, and Stefan D. Anker 7,19
1
VA San Diego Healthcare System, San Diego, CA, USA; 2University of California, San Diego, CA, USA; 3University Hospital Basel, Basel, Switzerland; 4Henry Ford Health System,
Detroit, MI, USA; 5The Cleveland Clinic, Cleveland, OH, USA; 6Medical University, Faculty of Public Health, Wroclaw, Poland; 7Charite, Campus Virchow-Klinikum, Berlin, Germany;
8
Virgina Commonwealth University, Richmond, VA, USA; 9University of California, San Francisco, CA, USA; 10University of Otago, Christchurch, New Zealand; 11Athens University
Hospital Attikon, Athens, Greece; 12Sant’Andrea Hospital, University La Sapienza, Rome, Italy; 13VA Minneapolis, MN, USA; 14University of Leicester, and Leicester NIHR
Cardiovascular Biomedical Research Unit, Leicester, UK; 15University of Maryland School of Medicine, Baltimore, MD, USA; 16University of California, San Diego School of Medicine,
San Diego, CA, USA; 17BRAHMS GmbH, Biotechnology Centre Hennigsdorf/Berlin, Germany; 18Waltraut Bergmann Foundation, Hohen Neuendorf, Germany; and 19Centre for
Clinical and Basic Research IRCCS San Raffaele, Roma, Italy
Received 3 September 2011; revised 5 November 2011; accepted 12 December 2011; online publish-ahead-of-print 2 February 2012
Aims
Biomarkers have proven their ability in the evaluation of cardiopulmonary diseases. We investigated the utility of concentrations of the biomarker procalcitonin (PCT) alone and with clinical variables for the diagnosis of pneumonia in
patients presenting to emergency departments (EDs) with a chief complaint of shortness of breath.
.....................................................................................................................................................................................
Methods
The BACH trial was a prospective, international, study of 1641 patients presenting to EDs with dyspnoea. Blood
and results
samples were analysed for PCT and other biomarkers. Relevant clinical data were also captured. Patient outcomes
were assessed at 90 days. The diagnosis of pneumonia was made using strictly validated guidelines. A model using
PCT was more accurate [area under the curve (AUC) 72.3%] than any other individual clinical variable for the diagnosis of pneumonia in all patients, in those with obstructive lung disease, and in those with acute heart failure (AHF).
Combining physician estimates of the probability of pneumonia with PCT values increased the accuracy to .86% for
the diagnosis of pneumonia in all patients. Patients with a diagnosis of AHF and an elevated PCT concentration
(.0.21 ng/mL) had a worse outcome if not treated with antibiotics (P ¼ 0.046), while patients with low PCT
values (,0.05 ng/mL) had a better outcome if they did not receive antibiotic therapy (P ¼ 0.049).
.....................................................................................................................................................................................
Conclusion
Procalcitonin may aid in the diagnosis of pneumonia, particularly in cases with high diagnostic uncertainty. Importantly,
PCT may aid in the decision to administer antibiotic therapy to patients presenting with AHF in which clinical uncertainty exists regarding a superimposed bacterial infection.
Trial registration: NCT00537628
----------------------------------------------------------------------------------------------------------------------------------------------------------Keywords
Acute heart failure † Procalcitonin † Pneumonia † Survival † Diagnosis
* Corresponding author. 3350 La Jolla Village Drive 151A San Diego, CA 92161, USA. Tel: +1 858 552 8585 x 7344, Fax: +1 858 552 7490, Email: [email protected]
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279
Procalcitonin for pneumonia diagnosis in patients with dyspnoea
Introduction
The chief complaint of shortness of breath in patients presenting
with suspected pneumonia requires a rapid accurate assessment
so that early antibiotic therapy can be initiated and appropriate
additional management implemented.1,2 The diagnosis of pneumonia can be difficult in patients with pre-existing parenchymal lung
disease because of baseline abnormal chest imaging, or in those
presenting with an acute exacerbation of obstructive lung disease
(AECOPD), bronchitis, or viral syndrome, because of overlapping
symptoms. Detecting superimposed pneumonia in patients presenting with acute heart failure (AHF) is additionally difficult
because of the non-specific nature of chest X-ray abnormalities
in the setting of cardiogenic pulmonary oedema. Unfortunately,
misdiagnoses may result in delayed or erroneous treatment potentially increasing adverse outcomes.3,4 Procalcitonin (PCT) expression in parenchymal tissue is induced by bacterial infection. PCT
is a diagnostic marker of severe bacterial infections. While PCT
release is not organ specific, concentrations have been successfully
used to diagnose and guide antibiotic therapy (initiation and cessation) in lower respiratory tract infections, pneumonia, and sepsis.5,6
However, PCT has never been extensively studied for its ability to
identify pneumonia or bacterial infection in the setting of AHF. We
hypothesized that in patients presenting with a chief compliant of
shortness of breath, PCT can significantly enhance the clinician’s
ability to diagnose pneumonia, whether occurring in isolation, or
in the setting of AHF or of AECOPD. We also evaluated the association between PCT concentration, antibiotic treatment, and
outcome in patients with a primary diagnosis of AHF, reasoning
that overlapping symptoms of cough, shortness of breath, and abnormal chest imaging make this group uniquely challenging for clinicians with regards to the diagnosis of superimposed pneumonia,
possibly leading to inappropriate antibiotic utilization.
Methods
The BACH (Biomarkers in Acute Heart Failure) trial was a prospective,
15-centre international study of 1641 patients presenting to the emergency department (ED) with a chief complaint of dyspnoea. All sites
received local Institutional Review Board approval. The primary
results have been reported elsewhere.7
Study population
A total of 1641 patients reporting shortness of breath upon presentation to the ED were enrolled from March 2007 to February 2008.
Patients were excluded if they were under 18 years of age, unable
to provide consent, had an acute ST-elevation myocardial infarction,
were receiving haemodialysis, or had renal failure. The emergency physicians (EPs), blinded to the results of the investigational markers,
assessed the patients enrolled in the study for the diagnosis of AHF
or pneumonia using two separate visual analogue scales (VASs) and
assigned a value of 0 – 100% clinical diagnostic certainty.
Confirmation of diagnosis
The gold standards for the diagnoses were determined by two cardiologists who independently reviewed all medical records and classified
the likely diagnosis as heart failure (HF), pneumonia, a combination of
both, or another diagnosis. Both were blinded to the other’s
assessments, investigational markers, and the EP’s diagnosis. In the
event of disagreement that could not be settled, a third adjudicator
was used. In cases of suspected pneumonia, diagnosed using modified
criteria from Fine et al. and Leroy et al.,8,9 a single pulmonologist,
blinded only to biomarker values, reviewed each case. All cases of
pneumonia were required to have a new infiltrate on chest imaging
in combination with microbiological proof of infection, or more than
one of the major criteria. In cases of equivocal chest imaging (interstitial pattern, effusions), at least two major criteria were necessary.
Community-acquired and healthcare-associated pneumonias were
both recorded as pneumonia.
Biomarker assessment
Procalcitonin and other biomarkers were quantified with an automated sandwich immunoassay using TRACE technology described previously.7 The PCT-sensitive KRYPTOR assay has a detection limit of
0.02 ng/mL and a functional assay sensitivity of 0.06 ng/mL. The interassay coefficient of variation for concentrations .0.3 ng/mL is ,6%.
Samples were processed by personnel blinded from any patient data.
White blood cell count (WBC) and C-reactive protein, if measured
as part of the clinician’s usual diagnostic workup, were potentially
known to the clinician at the time of VAS determination.
Statistical analysis
Values are expressed as means and standard deviations, medians and
interquartile ranges (IQR), or counts and percentages, as appropriate.
Patients with and without pneumonia were compared with independent samples t-tests or Fisher’s exact tests, as appropriate. Variables
were log-transformed if appropriate.
Logistic regression was used to evaluate PCT for the diagnosis of
pneumonia, for both uni- and multivariable analysis. To demonstrate
independence from clinical variables, the added value of PCT on top
of (i) a multivariable model with the seven most significant univariate
variables and (ii) the physician-estimated probability of pneumonia
was evaluated based on the likelihood ratio x2 test for nested
models. The concordance index [C index or area under the curve
(AUC)] is given as an effect measure for uni- and multivariable
models. For multivariable models, a bootstrap-corrected version of
the C index/AUC is given. For continuous variables, odds ratios
(ORs) were standardized to describe the OR for a change of one
IQR. The 95% confidence intervals (CIs) for risk factors and significance levels for x2 are given.10 Net reclassification improvements
(NRIs) were calculated for adding PCT based on categories which represent approximately the 15th and 85th percentile of the predicted
probability for the model excluding PCT.11 Receiver operating characteristic (ROC) curves were constructed to assess the sensitivity and
specificity of PCT and multivariable models to compare their ability
to predict pneumonia.
Cox proportional hazards regression was used to analyse the association of risk factors with survival in uni- and multivariable analyses.
The C index is given as an effect measure. Survival curves plotted by
the Kaplan– Meier method were used for illustration. For categorical
variables, log-rank test P-values are given. To account for systematic
differences between patients with and without antibiotic treatment,
survival rates were adjusted for covariates.10
All analyses utilized a two-sided P-value of 0.05 for significance.
All statistical analyses were performed using R version 2.5.1
(http://www.r-project.org) and Statistical Package for the Social
Sciences (SPSS) version 16.0 (SPSS Chicago, IL, USA).
280
Results
A total of 1641 patients were evaluated in the ED for shortness of
breath, of which 6.8% (112) received a primary diagnosis of pneumonia, 34.6% (568) AHF, 12.2% (201) chronic obstructive pulmonary disease (COPD) exacerbation, 7.0% (130) asthma
exacerbation, 6.5% (106) chest pain, 3.7% (61) bronchitis, 3.4%
(55) arrhythmia, 2.4% (39) acute coronary syndrome, 2.3% (38)
pulmonary embolism, 1.6% (27) influenza, and 18.5% (304) other
diseases. A total of 155 patients (9.4%) with a primary or secondary diagnosis of pneumonia (112 as primary, and 43 as secondary)
were combined. There were 407 patients (24.8%) diagnosed with
an AECOPD (271 with COPD and 136 with asthma).
Patient characteristics in the pneumonia group and the nonpneumonia group are shown in Table 1. Patients with pneumonia
were significantly more likely to have a past medical history of
COPD and pneumonia, and a recent history of cough and night
sweats. A past medical history of HF, coronary artery disease, myocardial infarction, and angioplasty was significantly more common
in patients without pneumonia. PCT levels were obtained for
99.4% of all patients. PCT ranged from 0.02 to 349 ng/mL, with a
median of 0.07 ng/mL and an IQR from 0.05 to 0.13 ng/mL.
In healthy subjects, PCT plasma concentrations are typically
,0.05 ng/mL.12
Procalcitonin for the diagnosis of
pneumonia
Overall, 155 (9.4%) patients had a primary or secondary diagnosis
of pneumonia. Twenty-nine of these had concurrent AHF and 17
had a concurrent AECOPD (13), asthma (2), or bronchitis (2).
For patients with pneumonia, median PCT concentration was
0.18 ng/mL (IQR 0.07–0.58), and for patients without pneumonia
PCT was 0.07 ng/mL (IQR 0.05–0.12). In total, 203 patients
were on antibiotics upon entry. Their median PCT concentration
was 0.08 ng/mL (IQR 0.05 –0.20). A univariate logistic regression
model for predicting the diagnosis of pneumonia was highly significant for PCT (x2 106.8, P , 0.0001, AUC 0.723). PCT predicted
pneumonia equally well in the subgroup of patients with a
history of lung disease (asthma or COPD) (AUC 0.713) and in
patients presenting with an AECOPD or bronchitis (AUC 0.715),
but slightly less in patients with concurrent AHF (AUC 0.641).
Multivariable analysis combining PCT values with clinical signs
(Table 2) demonstrated that PCT contributed significantly to the
prediction of pneumonia (P , 0.0001), adding independent information to clinical signs and increasing the AUC from 0.841 to
0.863 (x2 for adding PCT to the model 37.5, df ¼ 1, P , 0.0001).
Bootstrap-corrected AUCs were 0.834 for the model with clinical
signs only and 0.857 for the model including PCT (P , 0.0001).
Within this model, PCT was one of the strongest markers,
together with temperature and recent history of cough. PCT
was associated with a net reclassification improvement of 5.0%
(95% CI 4.0 –6.2%), based on risk categories representing approximately the 15th and 85th percentile of the predicted probability
for the model including clinical signs only. Overall, the model including PCT moved 2.9% of pneumonia into a higher probability
category, and 5.2% of non-pneumonia into a lower probability
category.
A. Maisel et al.
Similarly, in a model incorporating both log-transformed PCT
and physician-estimated probability, both variables contributed significantly to the prediction of pneumonia (P , 0.0001). PCT
increased the AUC from 0.850 to 0.864 (x2 for adding PCT to
the VAS 28.2, df ¼ 1, P , 0.0001). The bootstrap-corrected
AUC for the combined model including PCT was 0.863 (P ,
0.0001), with an NRI of 5.0% (95% CI 4.0 –6.2%). The model including PCT moved 2.1% of pneumonia into a higher probability
category, and 5.3% of non-pneumonia into a lower probability category. Figure 1 illustrates the predictive performance for PCT
alone, the multivariable model including clinical signs, and the combined model including PCT using ROC curve analysis.
Chest X-ray was performed in 1445 patients (88%), of which
144 (10%) had definitive findings consistent with pneumonia [sensitivity and specificity 64.0% (95% CI 55.6–71.6%) and 95.7% (95%
CI 94.4–96.6%), respectively]. PCT significantly added to the diagnostic value of the chest X-ray for the diagnosis of pneumonia,
improving the AUC from 0.798 (chest X-ray alone) to 0.864
(chest X-ray and PCT) (P , 0.0001)). Importantly, PCT remained
significant when the chest X-ray is included in the multivariable
clinical signs model (P , 0.0001).
Total leucocyte count (WBC) was a moderate predictor for
pneumonia, with an AUC of 0.69 (data not shown). This is consistent with findings from previous research.13 PCT added significantly
to the predictive value of WBC (added x2 74.5, P , 0.0001) indicating that PCT was better than and independent from WBC for
predicting pneumonia. Adding (log-transformed) WBC to the multivariable model in Table 2 does not affect the results (PCT
remained significant, P , 0.0001). Due to the missing values in
WBC, the available patients for the multivariable model were,
however, significantly reduced.
Decreasing clinical uncertainty in difficult
to diagnose pneumonia cases
At presentation, doctors were uncertain (defined as probability
estimates between 21% and 80%) about the presence of pneumonia in 30% of patients (n ¼ 499). In the 208 patients who presented
with a PCT value .0.25 ng/mL, a concentration that predicts
bacterial infection,12 the EP-estimated probability of pneumonia
was high (.80%) in only 15% of cases. The distributions of the
physician probability estimates are illustrated in Figure 2. This
figure also demonstrates how the uncertain category (labelled
‘Medium’) would be reduced by 82% if physician estimates were
combined with a model ruling out pneumonia when PCT values
were ,0.25 ng/mL.
Procalcitonin and the decision to give
antibiotics to patients presenting with
acute heart failure
PCT was significantly associated with 90-day all-cause mortality
for patients diagnosed with AHF (P ¼ 0.0024). Figure 3 illustrates
this relationship in a Kaplan – Meier plot of PCT quintiles.
Patients in the first quintile (PCT , 0.05 ng/mL) had a 90-day
survival rate of 92.0%, which fell to 80.5% (hazard ratio 2.6)
for patients in the fifth quintile (PCT . 0.21 ng/mL). One-fifth
(20.8%) of these patients diagnosed with AHF were also
281
Procalcitonin for pneumonia diagnosis in patients with dyspnoea
Table 1 Patient characteristics grouped by the presence or absence of pneumonia
Non-pneumonia (n 5 1486)
Pneumonia (n 5 155)
P-value
63.5 + 17.0
770 (51.8)
66.3 + 15.8
89 (57.4)
0.0517
0.2049
White
Black
966 (65.6)
451 (30.6)
124 (80.5)
25 (16.2)
Other
55 (3.7)
5 (3.2)
Variables
N
...............................................................................................................................................................................
Demographics
Age (years)
Male gender
1641
1641
Race
1626
0.0005
Recent history
Smoking
1593
420 (29.1)
49 (32.9)
0.3456
Wheezing
1543
430 (30.6)
38 (27.3)
0.4409
Cough
Weight gain
1603
1438
811 (54.6)
235 (17.9)
137 (88.4)
14 (10.9)
,0.0001
0.0497
Night sweats
1495
284 (20.9)
40 (29.2)
0.0294
Orthopnoea
Dyspnoea at rest
1536
1605
625 (44.8)
719 (49.6)
64 (45.7)
76 (49.4)
0.8587
1.0000
Heart rate (b.p.m.)
Systolic BP (mmHg)
1641
1641
89.8 + 23.6
140.6 + 30.4
101.6 + 22.8
133.5 + 31.3
,0.0001
0.0055
Diastolic BP (mmHg)
1641
80.8 + 18.2
74.9 + 19.9
0.0002
BMI (kg/m2)
Temperature (8C)
1399
1576
29.4 + 8.9
36.70 + 0.63
27.0 + 7.6
37.41 + 1.00
0.0034
,0.0001
Exam variables
Pulse oximetry (%)
1609
95.3 + 5.3
93.2 + 5.3
,0.0001
Rales
S3
1624
1580
452 (30.8)
43 (3)
72 (46.5)
1 (0.7)
0.0001
0.1175
Murmur
1604
226 (15.5)
28 (18.7)
0.3467
Elevated JVP
Oedema
1539
1615
256 (18.4)
548 (37.5)
15 (10.1)
40 (26.1)
0.0121
0.0060
Ascites
1579
38 (2.7)
3 (2)
0.7917
Wheezing
History variables
1619
397 (27.1)
55 (35.5)
0.0304
Arrhythmia
1555
365 (25.9)
40 (27.2)
0.7671
Asthma
CRI
1594
1584
289 (20.0)
222 (15.5)
29 (19.2)
24 (16.2)
0.9148
0.8117
HF
1597
531 (36.7)
38 (25.5)
0.0069
CAD
COPD
1587
1594
469 (32.6)
409 (28.4)
34 (23)
62 (40.8)
0.0159
0.002
DM
1621
427 (29.1)
35 (22.7)
0.1104
Hyperlipidaemia
Hypertension
1549
1614
529 (37.5)
987 (67.5)
41 (29.5)
93 (61.6)
0.0654
0.1470
MI
1584
282 (19.7)
18 (11.9)
0.0214
Pneumonia
Pulmonary embolism
1536
1604
196 (13.2)
75 (5.2)
64 (41.3)
10 (6.5)
0.0005
0.4482
CABG
1615
141 (9.7)
17 (11.0)
0.5687
Angioplasty/stent
Stroke/CVA
1602
1608
194 (13.4)
151 (10.4)
10 (6.6)
14 (9.0)
0.0147
0.6774
Pacemaker/ICD
1616
144 (9.9)
18 (11.6)
0.4820
1612
37 (2.5)
6 (3.9)
0.2935
Asprin
1616
526 (36.0)
48 (31.4)
0.2870
Clopidogrel
Warfarin
1617
1615
124 (8.5)
232 (15.9)
7 (4.6)
25 (16.3)
0.1175
0.9075
Prosthetic valve
Outpatient medications (known)
Continued
282
A. Maisel et al.
Table1 Continued
Variables
N
Non-pneumonia (n 5 1486)
Pneumonia (n 5 155)
P-value
...............................................................................................................................................................................
Beta-blockers
1613
580 (39.7)
47 (31.1)
0.0436
ACE inhibitors or ARBs
Calcium channel blockers
1616
1614
622 (42.5)
329 (22.5)
58 (37.9)
40 (26.1)
0.3019
0.3124
Statins
1617
485 (33.1)
32 (20.9)
0.0019
Diuretics
Digoxin
1618
1617
713 (48.7)
111 (7.6)
61 (39.9)
9 (5.9)
0.0412
0.5196
Aldosterone inhibitor
1615
139 (9.5)
10 (6.6)
0.3016
Anti-arrhythmics
Nebulizer/inhaler
1616
1613
84 (5.7)
497 (34.0)
8 (5.3)
64 (42.1)
1.000
0.0492
Steroids
1582
341 (23.8)
50 (33.3)
0.0127
Antibiotics
Smoking cessation therapy
1614
1577
161 (11.0)
27 (1.9)
42 (27.6)
1 (0.7)
,0.0001
0.5111
Other
1593
941 (65.2)
89 (59.3)
0.1523
Values are mean + SD or n (%) and compared with independent samples t-test or Fisher exact tests, respectively.
ACE, angiotensin-converting enzyme; ARB, angiotensin receptor blocker; BMI, body mass index; BP, blood pressure; CABG, coronary artery bypass grafting; CAD, coronary artery
disease; COPD, chronic obstructive pulmonary disease; CRI, chronic renal insufficiency; CVA, cerebral vascular accident; DM, diabetes mellitus; HF, heart failure; ICD, implantable
cardiac defibrillator; JVP, jugular venous pressure; MI, myocardial infarction.
Table 2 Prediction of pneumonia diagnoses (n 5 155 events) from signs, physician-estimated probability of pneumonia
(visual analogue scale), and procalcitonin concentration
Predictor
Univariable model results
................................................................
Multivariable model results
...............................................................
OR
95%CI
x2
Model with individual variables
Temperature (8C)
2.2
1.9–2.5
124.1
,0.0001
0.709
1.8
1.5– 2.1
45.5
,0.0001
PCT (log, ng/mL)
1.9
1.7–2.1
106.8
,0.0001
0.723
1.6
1.4– 1.9
38.7
,0.0001
Cough (yes)
Past pneumonia (yes)
6.7
4.7
4.0–11.4
3.3–6.7
76.2
65.0
,0.0001
,0.0001
0.668
0.647
4.7
3.7
2.6– 8.5
2.4– 5.7
25.5
34.5
,0.0001
,0.0001
Heart rate (b.p.m.)
1.7
1.4–2.1
31.4
,0.0001
0.651
1.6
1.2– 2.1
12.8
0.0004
Rales (yes)
Pulse oximetry (log, %)
1.9
0.9
1.4–2.7
0.8–0.9
15.0
12.2
0.0001
0.0005
0.578
0.659
1.5
1.0
1.0– 2.3
0.9– 1.1
3.3
0.6
0.0696
0.4397
Diastolic BP (mmHg)
0.7
0.6–0.9
11.2
0.0008
0.580
0.7
0.5– 0.9
7.2
0.0073
Model with summary variable
VAS pneumonia (%)
5.5
4.4–6.9
265.5
,0.0001
0.850
4.7
3.7– 6.0
163.0
,0.0001
PCT (log, ng/mL)
1.9
1.7–2.1
106.8
,0.0001
0.723
1.4
1.3– 1.7
27.2
,0.0001
P-value
AUC
OR
95% CI
x2
P-value
AUC
...............................................................................................................................................................................
Area under the curve (AUC) is bootstrap corrected for multivariable models.
BP, blood pressure; CI, confidence interval; OR, odds ratio; PCT, procalcitonin; VAS, visual analogue scale.
treated with antibiotics (excluding those already on antibiotics at
the time of presentation), while only 5.1% (29) of those were
ultimately diagnosed with pneumonia, suggesting the possibility
of unnecessary antibiotic therapy. Additionally, in patients not
treated with antibiotics on presentation, PCT predicted the possible need for antibiotic therapy during follow-up, representing a
potential missed opportunity to treat: 33.0% of patients with
PCT . with 17.3% of patients with PCT values ,0.21 ng/mL
(P , 0.01). Notably, of the 117 patients with PCT concentrations suggesting significant bacterial infection (.0.5 ng/mL), 38
(32%) did not receive antibiotic therapy. In this group, half
were diagnosed with AHF, highlighting the difficulty of diagnosing superimposed bacterial infection in patients presenting
with AHF.
283
Procalcitonin for pneumonia diagnosis in patients with dyspnoea
Figure 1 Receiver operating characteristic (ROC) curves for
the diagnosis of pneumonia (n ¼ 155 events), comparing procalcitonin (PCT), the multivariable model including clinical signs, as
well as the clinical signs model plus PCT.
Figure 3 Kaplan – Meier plot of procalcitonin (PCT) quintiles
for patients diagnosed with acute heart failure (AHF). PCT was
significantly associated with 90-day all-cause mortality for patients
diagnosed with AHF (Cox regression, x2 9.2, P ¼ 0.0024).
patients into subgroups based on PCT concentration revealed survival differences between those treated or not treated with antibiotics. Patients with a PCT concentration .0.21 ng/mL had
significantly worse survival if not treated with antibiotics (P ¼
0.046, Figure 4B). For AHF patients with PCT concentrations
between 0.05 and 0.21 ng/mL, antibiotic treatment did not affect
survival (P ¼ 0.36, Figure 4C). AHF patients with low PCT values
(,0.05 ng/mL, Figure 4D) had an increased mortality if they were
treated with antibiotics (P ¼ 0.049).
The utility of procalcitonin combined
with mid-regional pro atrial natriuretic
peptide in the differential diagnosis of
dyspneoa
Figure 2 Distribution of patients based on physician-estimated
probability of pneumonia (low, medium, and high) and how this
distribution would shift if a procalcitonin (PCT) concentration
of ,0.25 ng/mL was adopted as a model to rule out pneumonia.
Procalcitonin, antibiotic administration,
and survival in patients presenting with
acute heart failure
Unadjusted data showed that patients treated with antibiotics had
higher 90-day all-cause mortality than patients not treated (P ¼
0.032). After adjustment of covariates to control for group differences with respect to expected outcome, overall survival rates for
AHF patients were not different whether or not they were treated
with antibiotics (P ¼ 0.583, Figure 4A). However, dividing the AHF
Figure 5 illustrates schematically how the combination of midregional pro atrial natriuretic peptide (MR-proANP) [or brain
natriuretic peptide (BNP)] and PCT can be used to diagnose
acutely dyspnoeic patients with AHF and/or pneumonia: a
PCT . 0.1 ng/mL together with an MR-proANP .350 pmol/L
defined community-acquired pneumonia occurring with concomitant AHF; a PCT . 0.1 ng/mL with an MR-proANP ,100 pmol/L
defined community-acquired pneumonia alone, while an
MR-proANP .350 pmol/L and a PCT ,0.1 ng/mL defined HF
alone. A similar algorithm can be deduced for BNP. The median
and IQR for PCT and MR-proANP values in the four subgroups
are listed in the legend of Figure 5.
Discussion
Differentiating the diagnosis of pneumonia from that of HF can be
difficult, and disastrous consequences may occur from a mistake.3,4
Moreover, detecting superimposed pneumonia in patients
284
A. Maisel et al.
Figure 4 Kaplan– Meier plot for antibiotic treatment and all-cause mortality within 90 days for patients with acute heart failure (A; all AHF
patients) and subgrouped by procalcitonin (PCT) quintiles: PCT . 0.21 ng/mL (B; highest quintile, P ¼ 0.049), between 0.05 and 0.21 (C;
P ¼ 0.36, summarizing quintiles 2 – 4), and ,0.05 ng/mL (D; lowest quintile, P ¼ 0.046). Survival rate is adjusted for covariates; see Methods.
Figure 5 Median procalcitonin (PCT) and mid-regional pro atrial natriuretic peptide (MR-proANP) concentrations (left), as well as median
PCT and brain natriuretic peptide (BNP) concentrations (right) for patients with acute heart failure (AHF) alone, pneumonia alone, AHF and
pneumonia, and those without either condition. The median and interquartile range (IQR) for PCT (in ng/mL) in the four subgroups were as
follows: no AHF, no pneumonia 0.06 (0.04– 0.09); pneumonia, no AHF 0.23 (0.07– 0.92); AHF, no pneumonia 0.09 (0.06 –0.16); and AHF and
pneumonia 0.14 (0.09– 0.26). For MR-proANP (in pmol/L): no AHF, no pneumonia 84 (47 – 183); pneumonia, no AHF 141 (62– 240); AHF, no
pneumonia 417 (277 – 620); and AHF and pneumonia 398 (279– 635). For BNP (in ng/mL): no AHF, no pneumonia 50 (20 –166); pneumonia, no
AHF 122 (37 – 292); AHF, no pneumonia 766 (409 – 1461); and AHF and pneumonia 400 (210– 857).
presenting with AHF is additionally difficult because of the similar
and therefore non-specific physical exam and chest X-ray abnormalities. This can also lead to great morbidity and mortality.3,4
This study serves to highlight the difficulty clinicians face in diagnosing pneumonia in patients presenting with AHF.
In HF patients it is obviously important to identify those with the
highest risk in order to improve outcome. Superimposed pulmonary disease has been shown to lead to increased morbidity and
mortality in HF patients. Iverson and colleagues14 have shown a
clear link between lung function and outcome in HF patients by
demonstrating the prognostic importance of spirometric variables,
in addition to known risk factors, including self-reported COPD.
These authors posit the knowledge that patients who have
COPD may reduce the risk of overdosing diuretics due to
misinterpretation of the primary underlying cause of dyspnoea.
A similar argument can be made for those with pneumonia in our
study.
Our results suggest that PCT provides additive diagnostic information in patients presenting with shortness of breath; models
using PCT were robust when clinical uncertainty existed. Furthermore, our data speak to the potential utility of PCT in predicting
clinical benefit from antibiotic treatment. Patients with a PCT
concentration .0.21 ng/mL had significantly worse survival if
not treated with antibiotics, and those with PCT values
,0.05 ng/mL had an increased mortality if they were treated
with antibiotics, suggesting that the risks of antibiotic administration may outweigh the benefits in this group. Taken together,
these data suggest that the incorporation of PCT into the decision
285
Procalcitonin for pneumonia diagnosis in patients with dyspnoea
to administer antibiotic therapy to patients presenting with AHF
may impact survival.
Our data also demonstrate that combining PCT with
MR-proANP, which was reported in this population to aid in the
diagnosis of AHF,7 holds promise for the categorization of
acutely dyspnoeic patients into three groups mandating early
therapy, specifically those with either HF alone, pneumonia
alone, or both in combination. Figure 5 shows the median PCT
and MR-proANP/BNP concentrations in all patients, grouped by
the presence or absence of pneumonia and AHF, and underscores
how multiple biomarkers may be combined to assist in the diagnosis of clinically challenging overlapping disease states. Gheorghiade
et al. and colleagues have previously proposed that a wide range of
clinical and laboratory markers be used in combination to assess
and grade congestion in acute HF more optimally in order to
better direct management.15 It could be argued that improved
grading strategies, making optimal use of serum biomarkers,
would be equally if not more important in the acute management
of the undifferentiated dyspnoea that presents to EDs.
Limitations
There are several limitations of our study. Antibiotic treatment was
neither randomized nor standardized in terms of type and dosage,
and the analysis was post-hoc and not pre-specified. C-reactive
protein measurements, which are frequently used in the differential
diagnosis of HF vs. pneumonia, were not included in the analysis.
Due to the lack of randomization, it is probable that sicker patients
were also more likely to receive antibiotics, which probably
explains the observed difference in risk of death. The number of
patients ultimately diagnosed with both pneumonia and AHF was
low, possibly reflecting our broad inclusion criteria of undifferentiated dyspnoea, consistent with the primary endpoint of the trial.
This might also reflect a pre-selection bias of the on-site cardiologist responsible for making the initial assessment and therefore
some cases of pneumonia might have escaped detection. This
may also explain the slightly worse diagnostic performance in
patients with concurrent AHF.
Hence our conclusions are exploratory and need validation in a
randomized controlled trial. The rationale for carrying out this analysis in the large BACH dyspnoea trial was to evaluate whether
PCT could aid in the decision to initiate antibiotics in patients
with a primary diagnosis of AHF, where pneumonia occurs as a
relevant but possibly less apparent co-morbidity. If this is proven,
a logical next phase might then be a study treating according to
biomarker stratification.
Another potential limitation is that the confirmation of diagnosis
was performed by two independent cardiologists who made the
final diagnosis. This method of diagnosis has the potential for
bias of underreporting when two specialists from the same discipline are making the final diagnosis.
Clinical implications
This study extends the knowledge from previous work evaluating
the clinical utility of PCT in the diagnosis and management of
patients with suspected community-acquired pneumonia in the
setting where AHF is also a possibility. There is a strong likelihood
that PCT levels will help guide diagnostic therapeutic decisions
with antibiotics. This needs to be tested in a large randomized
interventional trial.
Conflict of interest: A.M., research support from Roche, Biosite,
and Bayer; consultant for Biosite. S.-X.N., consultant for ThermoFisher Scientific. C.M., research grants from The Swiss National
Science Foundation, the Swiss Heart Foundation, the Novartis
Foundation, the Krokus Foundation, Abbott, Biosite, BRAHMS,
Roche, and the University of Basel. R.M.N., research support
from BRAHMS. W.F.P., Scientific Advisory Board of Abbott,
Beckman-Coulter, Biosite, Inverness, Ortho Clinical Diagnostics,
and Response Biomedical; research grants from Abbott, Biosite,
and Inverness. M.R., Scientific Advisory Board of Inverness
Medical; travel support, honoraria, and research grants from
Roche Diagnostics and Inverness Medical (Biosite). G.S.F., research
support from Biosite, BRAHMS, and Roche. S.D.S., consultant for
Biosite. L.L.N., research support from BRAHMS, Inverness, and
Medical Innovations. L.B.D., research grant from Roche. O.H.,
employee of BRAHMS GmbH. A.B. and N.G.M., former employees
of BRAHMS GmbH. S.D.A., research support from BRAHMS; honoraria from Abbott and Biosite; consultant for BRAHMS. All other
authors have no conflicts to declare.
Authors’ contributions: all authors had full access to all of the
data (including statistical reports and tables) in the study and can
take responsibility for the integrity of the data and the accuracy of
the data analysis. A.M. and S.-X.N. contributed to collecting data,
design of the study, data analysis, and the writing of all sections of
the manuscript, and approved the final version of the manuscript.
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